Learning to solve nonlinear least squares for monocular stereo阅读笔记
1.通过神经网络计算迭代更新来实现更好的最值逼近【19】。 1.1使用长短期记忆回归神经网络（LSTM-RNN）来预测更新 。因为雅可比矩阵结构常常表现出特定的倾向性，在将雅可比矩阵引入网络之前对其作变换， 。通常优化计算算法中使用的是Hessian的近似，这篇文章使用LSTM-RNN网络来实现更强大的函数逼近能力。
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相关文章
- 1. 论文阅读笔记《Learning monocular depth estimation infusing traditional stereo knowledge》
- 2. Triangulation Learning Network: from Monocular to Stereo 3D Object Detection
- 3. 论文阅读笔记《End-to-End Learning of Geometry and Context for Deep Stereo Regression》
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